A Collision Avoidance Algorithm for Human Motion Prediction Based on Perceived Risk of Collision: Part 2-Application

Yang, James; Howard, Brad; Baus, Juan

doi:10.1080/24725838.2021.2004265

Cited by 4 publications

(1 citation statement)

References 27 publications

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“…Zaman et al 21,39 proposed a dynamic-joint-strength based 3D model to predict maximum lifting weight and motion. Howard, 60 Yang and Howard, 61 and Yang et al 62,63 reported the addition of cognitive principles into the optimization-based posture and motion prediction formulations considering perceived risk of collision.…”

Section: Human Modeling For Liftingmentioning

confidence: 99%

Optimization-based biomechanical lifting models for manual material handling: A comprehensive review

Zaman

Arefeen

Quarnstrom

et al. 2022

Proc Inst Mech Eng H

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Lifting is a main task for manual material handling (MMH), and it is also associated with lower back pain. There are many studies in the literature on predicting lifting strategies, optimizing lifting motions, and reducing lower back injury risks. This survey focuses on optimization-based biomechanical lifting models for MMH. The models can be classified as two-dimensional and three-dimensional models, as well as skeletal and musculoskeletal models. The optimization formulations for lifting simulations with various cost functions and constraints are reviewed. The corresponding equations of motion and sensitivity analysis are briefly summarized. Different optimization algorithms are utilized to solve the lifting optimization problem, such as sequential quadratic programming, genetic algorithm, and particle swarm optimization. Finally, the applications of the optimization-based lifting models to digital human modeling which refers to modeling and simulation of humans in a virtual environment, back injury prevention, and ergonomic safety design are summarized.

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